Neurodynamics, Neuroimaging & Brains

Włodzisław Duch

Neurocognitive Laboratory, Center for Modern Interdisciplinary Technologies,

& Department of Informatics, Nicolaus Copernicus University

Google: Wlodzislaw Duch

25th ICANN, Barcelona, 6-9.09.16

The story

Trying to address a few very important questions. 1. How can we understand mind-body relations?

Questions Levels of description, neurocognitve phenomics.

2. Neuropsychiatry? Neurodynamics and ASD-ADHD, RDoC. 3. General behavior of people?

Associations, creativity, memory distortions, conspiracy theories, polarization of opinions, learning new domains.

4. How can we understand developmental processes? Auditory perception, working memory, therapeutic games, dyscalculia, sensory imagery.

5. How to use neurodynamics, connectomics, neuroimaging, brain fingerprinting in practical applications.

Bio Nano

Info

Cognitive

Neuro

Most important 21 century technologies

Ultimate technology: NeuroCognitive Informatics in Nano-hardware.

Duch W, Mandziuk J (Eds.), Challenges for Computational Intelligence.Springer "Studies in Computational Intelligence" Series, Vol. 63, 2007. Jankowski N, Duch W, Grąbczewski K, Meta-learning in Computational Intelligence. Springer 2011.

Center of Modern Interdisciplinary Technologies

Why am I interested in this?

Bio + Neuro + Cog Sci + Physics =>

NeuroCognitive Lab.

Other labs: molecular biology, chemical analytics, nanotech and electronics.

Main theme: maximizing human potential. Goal: understanding brain-mind relations, with a lot of help from computational intelligence; pushing the limits of brain plasticity. Big challenge! Funding: national/EU grants.

A group of neurofanatics

Our toys

Geometric model of mindObjective Subjective.

Brain Mind.Neurodynamics describes state of the brain activation measured using EEG, MEG, NIRS-OT, PET, fMRI or other techniques.

Mind states=f(Brain states)

How to represent mind state? In the space based on dimensions that have subjective interpretation: intentions, emotions, qualia. Mind state and brain state trajectory should then be linked together by some transformations. Intentions are uncovered by the Brain-Computer Interfaces. Lack of neurophenomenology.

Neurocognitive informaticsUse inspirations from the brain, derive practical algorithms!

My own attempts - see my webpage, Google: W. Duch

1. Mind as a shadow of neurodynamics: geometrical model of mind processes, psychological spaces providing inner perspective as an approximation to neurodynamics.

2. Intuition: learning from partial observations, solving problems without explicit reasoning (and combinatorial complexity) in an intuitive way.

3. Neurocognitive linguistics: how to find neural pathways in the brain.

4. Creativity in limited domains + word games, good fields for testing.

Duch W, Intuition, Insight, Imagination and Creativity, IEEE Computational Intelligence Magazine 2(3), 2007, pp. 40-52

PhenomicsPhenomics is the branch of science concerned with identification and description of measurablephysical, biochemical and psychological traits of organisms. Genom, proteom, interactom, exposome, virusom , connectom … omics.org has a list of over 400 various …omics !

Human Genome Project, since 1990. Human Epigenome Project, since 2003.Human Connectome Project, since 2009.Developing Human Connectome Project, UK 2013 + many others.

Behavior, personality, cognitive abilities <= phenotypes at all levels. Still many white spots on maps of various phenomes.

Can neurocognitive phenomics be developed to understand general behavior of people? Where should we start?

From Genes to Neurons

Genes => Proteins => receptors, ion channels, synapses => neuron properties, networks, neurodynamics

=> cognitive phenotypes, abnormal behavior, syndromes.

From Neurons to Behavior

Genes => Proteins => receptors, ion channels, synapses => neuron properties, networks

=> neurodynamics => cognitive phenotypes, abnormal behavior!

NeuropsychiatricPhenomics in 6 LevelsConsortium for Neuropsychiatric Phenomics (CNP)/NIMH RoDC approach:

Research Domain Criteria (RoDC) analyzes 5 large brain systems – negative/positive valence systems, arousal, cognitive, affective systems – through interaction of Genes, Molecules, Cells, Circuits, Physiology, Behavior, Self-Report, and Research Paradigms. From genes to cognitive subsystems and behavior, neurons and networks are right in the middle of this hierarchy.=> Neurodynamics is the key!

RDoC Matrix for Negative Valence Systems5 Psychological Constructs/Subconstructs:

Acute Threat ("Fear"); Potential Threat ("Anxiety"); Sustained Threat; Loss, Frustrative Nonreward

Each characterized by 8 aspects at different phenomic levels:

Genes Mole-cules Cells Circuits Physio-

logyBeha-vior

Self-Report

Para-digms

Ex: Sustained Threat Physiology: Dysregulated HPA axis | Error-related negativity Behavior: Anhedonia/decreased appetitive behavior | Anxious Arousal Attentional bias to threat | Avoidance | Decreased libido | Helplessness behavior | Increased conflict detection | Increased perseverative behavior | Memory retrieval deficits | Punishment sensitivity

Neurocognitive PhenomicsPhenotypes may be described at many levels. Ex. from top down: learning styles - education, psychiatry & psychology,neurophysiology, connectomes, microcircuits, neural networks, neurobiology - organs, tissues, cells, biophysics/chemistry & bioinformatics. Neurocognitive phenomics is even greater challenge than neuropsychiatric phenomics. Effects are more subtle but this is the only way to understand fully human/animal behavior. Data driven science! Genes, proteins,

epigenetics

Signaling pathways

Synapses, neurons & glia cells

Neural networks

Tasks, reactions

Cognition

Learning styles

Many types of neurons

Neurotransmitters & modulators

Genes & proteins, brain bricks

Specialized brain areas, minicolumns

Sensory & motor activity, N-back …

Memory types,attention …

Learning styles, strategies

Neurophenomics Research Strategy

The Consortium for Neuropsychiatric Phenomics (2008): bridge all levels, one at a time, from environment to syndromes.

Our strategy: identify biophysical parameters of neurons required for normal neural network functions and leading to abnormal cognitive phenotypes, symptoms and syndromes. • Start from simple neurons and networks, increase complexity.• Create models of cognitive function that may reflect some of the

symptoms of the disease, for example problems with attention. • Test and calibrate the stability of these models in a normal mode.• Determine model parameter ranges that lead to similar symptoms.• Relate these parameters to the biophysical properties of neurons.

Result: mental events at the network level are described in the language of neurodynamics and related to low-level neural properties. Example: relation of ASD/ADHD symptoms to neural accommodation.

Structural connectivity Functional connectivity

Graph theory

Signal extraction

Correlation matrix

Binary matrix

Whole-brain graph

Correlation calculation

Human connectome and MRI/fMRI

Bullmore & Sporns (2009)

Node definition

Path & efficiency

Clustering

Degree

d=3

d=2

Modularity

Genes=>Proteins

~5x1013=50T cells, 2m DNA/cell ~1014m=100 T km = 666 au!

~1015=1P synapses; >1M new synapses/sec~100G neuronów (1011)>550.000 structures in Swiss-proteome database~60.000 protein families~20.000 genes>100.000 proteins/cells

Organism is a process! lifetime 4 days to >100 years.

Ion channel types

Voltage-gated ion channels

Computational Models: NeurodynamicsModels at various level of detail.

• Minimal model includes neurons with 3 types of ion channels.

Models of attention:

• Posner spatial attention; • attention shift between visual objects. Models of word associations:

• sequence of spontaneous thoughts.

Models of motor control.

Critical: control of the increase in intracellular calcium, which builds up slowly as a function of activation. Initial focus on the leak channels, 2-pore K+, looking for genes/proteins.

Model of reading & dyslexia

Learning: mapping one of the 3 layers to the other two.Fluctuations around final configuration = attractors representing concepts.How to see properties of their basins, their relations?Model in Genesis: more detailed neuron description.

Emergent neural simulator:Aisa, B., Mingus, B., and O'Reilly, R. The emergent neural modeling system. Neural Networks,

21, 1045-1212, 2008.

3-layer model of reading: orthography, phonology, semantics, or distribution of activity over 140 microfeatures defining concepts. Hidden layers in between.

„Gain”: trajectory of semantic activations quickly changes to a new synchronized activity, periodically returns to the first basin of attraction.

Fuzzy Symbolic Dynamics (FSD)Complementing information in RPs:

RP plots S(t,t0) values as a matrix; FSD

1. Standardize data.2. Find cluster centers (e.g. by k-means algorithm): m1, m2 ...

3. Use non-linear mapping to reduce dimensionality:

Localized membership functions yk(t;W):

sharp indicator functions => symbolic dynamics; x(t) => strings of symbols;soft membership functions => fuzzy symbolic dynamics, dimensionality

reduction Y(t)=(y1(t;W), y2(t;W)) => visualization of high-dim data.

We may then see visualization of trajectory in some basin of attraction. Such visualizations are simply referred to as “attractors”.

T 1( ; , ) expkk k k k ky t t t μ x μ x μ

Fuzzy Symbolic Dynamics (FSD)Complementing information in RPs:

RP plots S(t,t0) values as a matrix; FSD

1. Standardize data.2. Find cluster centers (e.g. by k-means algorithm): m1, m2 ...

3. Use non-linear mapping to reduce dimensionality:

Localized membership functions yk(t;W):

sharp indicator functions => symbolic dynamics; x(t) => strings of symbols;soft membership functions => fuzzy symbolic dynamics, dimensionality

reduction Y(t)=(y1(t;W), y2(t;W)) => visualization of high-dim data.

We may then see visualization of trajectory in some basin of attraction. Such visualizations are simply referred to as “attractors”.

T 1( ; , ) expkk k k k ky t t t μ x μ x μ

Fast transitions

Attention is focused only for a brief time and than moved to the next attractor basin, some basins are visited for such a short time that no action may follow, no chance for other neuronal groups to synchronize. This corresponds to the feeling of confusion, not being conscious of fleeting thoughts.

Autism-Normal-ADHDb_inc_dt = 0.005 b_inc_dt = 0.01 b_inc_dt = 0.02

Inhibition

Increasing gi from 0.9 to 1.1 reduces the attractor basin sizes and simplifies trajectories.

Strong inhibition, empty head …

Long trajectories

Recurrence plots and MDS visualization in 40-words microdomain, starting with the word “flag”.

PDP for transitions starting from „flag”

Graph of transitions between attractors after 10 runs.

Why these particular transitions?

Connected attractors share some micro- features, some circuits (units) are deactivated, but visualization using RP or FSD does not show such details. The landscape of available attractors is also dynamic! Transition probabilities change, dimensions (features) are rescaled.

MDS word mappingMDS representation of all 40 words, showing similarities of their 140 dimensional vectors.

Attractors are in some cases far from words.

Transition Flag => rope => flea ... Can we make semantic map of concepts in real brains? See trajectories of thought?

Brain modules and cognitive processesSimple and more difficult tasks, requiring the whole-brain network reorganization.

K. Finc et al (HBM, in rev, with World Hearing Center, MPI for Human Development).

Left: 1-backRight: 2-back

Average over 35 participants.

Left and midline sections.

Brain modules and cognitive processesSimple and more difficult tasks, requiring the whole-brain network reorganization.

K. Finc et al (HBM, in rev).

Left: 1-back Top: connector hubsRight: 2-backBottom: local hubs

Average over 35 participants.

Dynamical change of the landscape of attractors? Less local, more global binding.

Activation of concepts in our minds leads to specific brain structure activity; each structure is involved in interpretation of many concepts (Gallant lab).

Activation of specific concept (mental state) leads to activation of specific brain areas and networks. Each such activation pattern contributes to sematic interpretation of many concepts through global brain activity.

This activation is sparse and may be better observed by looking at the flattened cortex: http://gallantlab.org/brainviewer/huthetal2012/

Nicole Speer et al. Reading Stories Activates Neural Representations of Visual and Motor Experiences. Psychological Science 2009; 20(8): 989–999.Meaning: always slightly different, depending on the context, but still may be clusterized into relatively samll number of distinct meanings.

Source localization maps brain activity to attractor dynamics.

Problem: these sources pop up and vanish in different places.

Fig. from: Makeig, Onton, 2009ERP Features and EEG Dynamics: An ICA Perspective.

Brain fingerprinting: discover in EEG specific patterns for attractor dynamics = subnetwork activation.

Origin of the learning styles

Connectomes develops before birth and in the first years of life.Achieving harmonious development is very difficult and depends on low-level (genetic, epigenetic, signaling pathways) processes, but may be influenced by experience and learning. • Excess of low-level (sensory) processes SS. • Poor CC neural connections and synchronization, frontalparietal

necessary for abstract thinking, weak functional connections prefrontal lobe other areas.

• Patterns of activation in the brain differ depending on whether the brain is doing social or nonsocial tasks.

• “Default brain network” involves a large-scale brain network (cingulate cortex, mPFC, lateral PC), shows low activity for goal-related actions; strong activity in social and emotional processing, mindwandering, daydreaming.

Origin of the learning styles

Connectomes develops before birth and in the first years of life.Achieving harmonious development is very difficult and depends on low-level (genetic, epigenetic, signaling pathways) processes, but may be influenced by experience and learning. • Excess of low-level (sensory) processes SS. • Poor CC neural connections and synchronization, frontalparietal

necessary for abstract thinking, weak functional connections prefrontal lobe other areas.

• Patterns of activation in the brain differ depending on whether the brain is doing social or nonsocial tasks.

• “Default brain network” involves a large-scale brain network (cingulate cortex, mPFC, lateral PC), shows low activity for goal-related actions; strong activity in social and emotional processing, mindwandering, daydreaming.

Learning styles

David Kolb, Experiential learning: Experience as the source of learning and development (1984), and Learning Styles Inventory.

Connectome and learning stylesSimple connectome models may help to connect and improve learning classification of the styles.S, Sensory level, occipital, STS, and somatosensory cortex; C, central associative level, abstract concepts that have no sensory components, mostly parietal, temporal and prefrontal lobes.

S=Sensory

C=CentralM=Motor

World

M, motor cortex, motor imagery & physical action. Frontal cortex, basal ganglia. Even without emotion and reward system predominance of activity within or between these areas explains many learning phenomena.

Learning styles D1Kolb passive-active dimension, observation – experimentation: motor-central processes MC, sensory-motor processes MS. Autistic people: processes at the motor level MM, leads to repetitive movements, echolalia.

S=Sensory

C=CentralM=Motor

World

The Learning Styles Inventory is a tool to determine learning style. The tool divides people into 4 types of learners: • divergers (concrete, reflective), • assimilators (abstract, reflective), • convergers (abstract, active), • accommodators (concrete, active).

Learning styles D2 Kolb perception-abstraction: coupling within sensory SS areas, vs. coupling within central CC areas.

Strong C=>S leads to vivid imagery dominated by sensory experience.

Autism: vivid detailed imagery, no generalization.

S=Sensory

C=CentralM=Motor

World

Attention = synchronization of neurons, limited to S, perception SS strongly binds attention => no chance for normal development. Asperger syndrome strong C=>S activates sensory cortices preventing understanding of metaphoric language. If central CC processes dominate, no vivid imagery but efficient abstract thinking is expected - mathematicians, logicians, theoretical physicist, theologians and philosophers ideas.

4 styles and more

Assimilators think and watch: prone to abstract thinking, reflective observation, inductive reasoning due to strong connections S=>C and within CC, weak connections from S=>M and C=>M.

Convergers combine abstract conceptualization, active experimentation, using deductive reasoning in problem solving. Strong CC and C=>M flow of activity. Divergers focus on concrete experience SS, strong CS connections and CC activity facilitating reflective observation, strong imagery, novel ideas but weak motor activity. Accommodators have balanced sensory, motor and central processes and thus combine concrete experience with active experimentation supported by central processes SCM. The idea of learning styles is criticized because there was no theoretical framework behind it, but objective tests of the learning styles may be based on brain activity.

Creativity with wordsThe simplest testable model of creativity (~ Campbell BVSR): • create interesting novel words that capture some features of products;• understand new words that cannot be found in the dictionary.

Model inspired by the putative brain processes when new words are being invented starting from some keywords priming auditory cortex.

Phonemes (allophones) are resonances, ordered activation of phonemes will activate both known words as well as their combinations; context + inhibition in the winner-takes-most leaves only a few candidate words.

Creativity = network+imagination (fluctuations)+filtering (competition)

Imagination: chains of phonemes activate both word and non-word representations, depending on the strength of the synaptic connections. Filtering: based on associations, emotions, phonological/semantic density.

discoverity = {disc, disco, discover, verity} (discovery, creativity, verity)digventure ={dig, digital, venture, adventure} new! Visual: Google Deep Dream hallucinations – but video streams not natural.

Creativity with wordsThe simplest testable model of creativity (~ Campbell BVSR): • create interesting novel words that capture some features of products;• understand new words that cannot be found in the dictionary.

Model inspired by the putative brain processes when new words are being invented starting from some keywords priming auditory cortex.

Phonemes (allophones) are resonances, ordered activation of phonemes will activate both known words as well as their combinations; context + inhibition in the winner-takes-most leaves only a few candidate words.

Creativity = network+imagination (fluctuations)+filtering (competition)

Imagination: chains of phonemes activate both word and non-word representations, depending on the strength of the synaptic connections. Filtering: based on associations, emotions, phonological/semantic density.

discoverity = {disc, disco, discover, verity} (discovery, creativity, verity)digventure ={dig, digital, venture, adventure} new! Visual: Google Deep Dream hallucinations – but video streams not natural.

How to become an expert?Textbook knowledge in medicine: detailed description of all possibilities.Effect: neural activation flows everywhere and correct diagnosis is impossible. Correlations between observations forming prototypes are not firmly established. Expert has only correct, “intuitive” associations; deep attractors = .Example: 3 diseases, vector NLP on clinical case description, MDS visualization.1) System that has been trained on textbook knowledge: weak attractors. 2) Same system that has learned later on real cases: deeper attr, still connected. 3) Same system that has learned only on description of real cases: deep attr.

Conclusion: abstract presentation of knowledge in complex domains leads to poor expertise, random real case learning is a bit better, learning with real cases that cover the whole spectrum of different cases is the best.

I hear and I forget.I see and I remember.I do and I understand.

Confucius, -500 r.

Conspiracy in the brain

Slow and rapid scenarios are possible, here only rapid presented: • Emotional situations => neurotransmitters =>

neuroplasticity => fast learning, must be important.• Fast learning => high probability of wrong interpretation. • Traumatic experiences, hopelessness, decrease brain plasticity and leave

only strongest association – strongly connected pathways. • Conspiracy theories form around such associations,

“frozen” pathways lead to brain activations forming strong attractors, distorting rational thinking.

• Such strong associations save brain energy and cannot be changed by rational arguments, that influence weaker associations only.

• This explanation becomes so obviously obvious … Model: concept vectors derived from a corpus + MDS or Growing Neural Gas visualization (Martinetz & Schulten, 1991). Social cognitive neuroscience should go more in this direction.

Internalization of environmentEpisodes are remembered and serve as reference points, if observations are unbiased they reflect reality, creating correct associations.

Extreme plasticityBrain plasticity (learning) is increased if long, Slow strong emotions are involved. Followed by depressive mood it leads to severe distortions, false associations, simplistic understanding.

Conspiracy viewsIlluminati, masons, Jews, UFOs, or twisted view of the world leaves big holes and admits simple explanations that save mental energy, creating „sinks” that attract many unrelated episodes.

Memoids …

Totally distorted world view, mental processes are reduced to a memplex …Ready to sacrifice oneself for a great idea.

SOM on real newspaper data

Different groups of people read different newspapers, are exposed to different media and social networks, resulting in different network of concepts and sharp polarization of opinions.

Big sinks attract neurodynamics manifesting in strong automatic associations with core concepts.

Different associative networks make communication almost impossible. Work in progress (with J. Szymanski et al.)

BabyLab, Neurocognitive LaboratoryCMIT NCU

Development of speech perception in infants, discrimination of phonemes, development of auditory working memory – the key to unfold human potential, boost motivation and foundation for learning.

Abstract thinking

Where intelligence comes from? speed of thinking + working memory + synaptic density reflected in ERP structure, later specific structure of connections will increase IQ, decrease creativity.

Strong correlation of IQ between the ability to order two very short sounds, one with higher pitch (ex. J. Dreszer, our lab).

Lynn-Flynn effect: IQ grows everywhere in the world, 24 points in USA since 1918, 27 points in UK.Toys and nutrition help to develop better brains?

Infants, syllablesBrains of newborns react to ba/ga/da syllables in the 3–5 day of life in a way that allows for prediction of problems with learning to read years later.(D. Molfesse, 2000).

Can we change it by training infants? We hope so, this may be verified using ERPs.Infant’s EEG is full of artifacts, big variance, longer phonemes give complex ERPs.

Infants, syllablesBrains of newborns react to ba/ga/da syllables in the 3–5 day of life in a way that allows for prediction of problems with learning to read years later.(D. Molfesse, 2000).

Can we change it by training infants? We hope so, this may be verified using ERPs.Infant’s EEG is full of artifacts, big variance, longer phonemes give complex ERPs.

Deaf people playing music?

The World Hearing Institute (Warsaw) has been organizing music festivals where deaf people (mostly children) have performed. Speech understanding and music perception by deaf children with cochlear implants – filtering EEG artefacts.

Dyscalculia screening/therapyUnderstanding numerosity, a new complex brain function, involves HIPS structure. 6-10% of children in primary education suffer from dyscalculia, specific difficulties with learning mathematical concepts. Rarely diagnosed, most countries have no screening tests.

Goals: 1. Introduce screening test for the preschool/first class to identify children with the risk of dyscalculia.

2. Short intensive training using computer game to associate mental number line with spatial dimensions.

Further therapy is done by experts.

Understanding by creating brains

• “Here, we aim to understand the brain to the extent that we can make humanoid robots solve tasks typically solved by the human brain by essentially the same principles. I postulate that this ‘Understanding the Brain by Creating the Brain’ approach is the only way to fully understand neural mechanisms in a rigorous sense.”

• M. Kawato, From ‘Understanding the Brain by Creating the Brain’ towards manipulative neuroscience. Phil. Trans. R. Soc. B 27 June 2008 vol. 363 no. 1500, pp. 2201-2214

• Humanoid robot may be used for exploring and examining neuroscience theories about human brain.

• Engineering goal: build artificial devices at the brain level of competence.

The Great Artificial Brain RaceBLUE BRAIN, HBP: École Polytechnique Fédérale de Lausanne, in Switzerland, use an IBM supercomputer to simulate minicolumn.

C2: 2009 IBM Almaden built a cortical simulator on Dawn, a Blue Gene/P supercomputer at Lawrence Livermore National Lab. C2 simulator re-creates 109 neurons connected by 1013 synapses, small mammal brain.

NEUROGRID: Stanford (K. Boahen), developing chip for ~ 106 neurons and ~ 1010 synapses, aiming at artificial retinas for the blind.

IFAT 4G: Johns Hopkins Uni (R.Etienne-Cummings) Integrate and Fire Array Transceiver, over 60K neurons with 120M connections, visual cortex model.

Brain Corporation: San Diego (E. Izhakievich), neuromorphic vision.

BRAINSCALES: EU neuromorphic chip project, FACETS, Fast Analog Computing with Emergent Transient States, now BrainScaleS, complex neuron model ~16K synaptic inputs/neuron, integrated closed loop network-of-networks mimicking a distributed hierarchy of sensory, decision and motor cortical areas, linking perception to action.

Neuromorphic computersSynapse 2015: IBM TrueNorth chip ~1M neurons and ¼G synapses, ok 5.4G tranzystorów. NS16e module=16 chips=16M neurons, >4G synapses, requires only 1.1 W! Scaling: 256 modules, ~4G neurons, ~1T= 1012 synapses < 300 W power! IBM Neuromorphic System can reach complexity of the human brain.

Few InitiativesIEEE Computational Intelligence Society Task Force (J. Mandziuk & W. Duch),

Towards Human-like Intelligence.

IEEE SSCI The 4th IEEE Symposium on Computational Intelligence for Human-like Intelligence, Athens, Greece, 6-9.12.2016.World Congress of Computational Intelligence 2014, Special Session: Towards Human-like Intelligence (A-H Tan, J. Mandziuk, W .Duch)

Brain-Mind Institute Schools, International Conference on Brain-Mind (ICBM) and Brain-Mind Magazine (Juyang Weng, Michigan SU).

AGI: conference, Journal of Artificial General Intelligence comments on Cognitive Architectures and Autonomy: A Comparative Review (eds. Tan, Franklin, Duch).

BICA: Annual International Conf. on Biologically Inspired Cognitive Architectures, 3rd Annual Meeting of the BICA Society, Palermo, Italy, 31.10- 3.11.2012

Conclusions

Grand challenges are facing usat every level!

Neurodynamics and neurocognitive phenomics is the key.

Is there a shorter route to understand human behavior?

I do not think so …

Duch W, Brains and Education: Towards Neurocognitive Phenomics (2013)

Soul or brain: what makes us human?

Interdisciplinary Workshop, Torun 19-21.10.2016

Infants, learning, and cognitive development.29-30.10.16 Interdoctor: Disorders of consciousness . 19-21.10.16

Thank you for synchronization of

your neurons!

Google: Wlodzislaw Duch => papers, talks, lectures …

DI NCU Projects: CI

Computational intelligence (CI), main themes: • Foundations of computational intelligence: meta-learning, transformation

based learning, k-separability, learning hard boole’an problems.• Novel learning: projection pursuit networks, QPC (Quality of Projected

Clusters), search-based neural training, Universal Learning Machines for transfer learning/learning from others (ULM), Support Feature Machines (SFM), almost Random Projection Machines (aRPM ), and more ...

• Understanding of data: prototype-based rules, visualization of NN function and visualization of dynamic trajectories.

• Similarity based framework for metalearning, heterogeneous systems, new transfer functions for neural networks.

• Feature selection, extraction, creation of enhanced spaces.• General meta-learning, or learning how to learn, deep learning.

DI NCU Projects:NCI

Neurocognitive Informatics: understanding complex cognition => creating algorithms that work in similar way.

• Computational creativity, insight, intuition, imagery.• Imagery agnosia, especially imagery amusia.• Neurocognitive approach to language, word games. • Medical information retrieval, analysis, visualization. • Comprehensive theory of autism, ADHD, phenomics. • Visualization of high-D trajectories, EEG signals, neurofeedback.• Brain stem models & consciousness in artificial systems.• Geometric theory of brain-mind processes. • Infants: observation, guided development. • Neural determinism, free will & social consequences.